Mechanistically Coupled PK (MCPK) Model of Dabrafenib Metabolism

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		Marco Albrecht	+ 1595 word(s)	1595	2022-01-28 03:46:47	\|
2	format is correct	Yvaine Wei	+ 1 word(s)	1596	2022-01-29 02:37:43	\|

This entry is adapted from the peer-reviewed paper 10.3390/pharmaceutics14020310

Dabrafenib inhibits the cell proliferation of metastatic melanoma with the oncogenic BRAF(V600)-mutation. However, dabrafenib monotherapy is associated with pERK reactivation, drug resistance, and consequential relapse. A clinical drug-dose determination study shows increased pERK levels upon daily administration of more than 300 mg dabrafenib. To clarify whether such elevated drug concentrations could be reached by long-term drug accumulation, the pharmacokinetics (MCPK) of dabrafenib and its metabolites were mechanistically coupled. The MCPK model is qualitatively based on in vitro and quantitatively on clinical data to describe occupancy-dependent CYP3A4 enzyme induction, accumulation, and drug–drug interaction mechanisms. The prediction suggests an eight-fold increase in the steady-state concentration of potent desmethyl-dabrafenib and its inactive precursor carboxy-dabrafenib within four weeks upon 150 mg b.d. dabrafenib. While it is generally assumed that a higher dose is not critical, it was found experimentally that a high physiological dabrafenib concentration fails to induce cell death in embedded 451LU melanoma spheroids.

CYP3A4 dabrafenib MCPK PK DDI enzyme induction metabolism

1. Introduction

Melanoma is a cancer type that develops from the pigment-producing melanocytes within the skin. Until recently, metastatic melanoma was considered refractory to treatment with a 3-year survival below 10%. A better understanding of the genetic alterations in metastatic melanoma cells has fundamentally changed systemic therapy and significantly improved the prognosis of patients. The serine-threonine kinase BRAF represents an integral component of the mitogen-activated RAF-MEK-ERK signal transduction pathway ^[1]^[2]. Activating mutations of the proto-oncogene BRAF (mutBRAF/wtNRAS, ∼60% of patients) lead to uncontrolled tumor growth ^[3]. Combinations of mutBRAF inhibitors plus MEK inhibitors are currently accredited in the clinic to treat mutBRAF melanoma, showing a disease control rate of ∼95% and improved median survival ^[4]^[5]. However, the vast majority of patients acquire resistance, resulting in tumor relapse.

In this context, a dose determination study revealed higher doses of the mutation-specific BRAF inhibitor dabrafenib to correlate with an increased expression of proliferation markers putatively contributing to tumor relapse. Accordingly, downregulation of ERK phosphorylation (pERK) was shown to be most effective in response to a daily dose of 300 mg dabrafenib, whereas elevated doses of 400 mg and 600 mg, respectively, only presented with reduced pERK inhibition, as determined in human melanoma tissue samples ^[6]. This observation might be of critical relevance because periodically administered dabrafenib may accumulate in the blood plasma to finally reduce the therapeutically desired pERK inhibition. Dabrafenib itself does not accumulate, but the published dabrafenib pharmacokinetics (PK) model does not include dabrafenib metabolites ^[6]^[7].

The occurring enzyme induction requires a stringent modeling workflow as the European Medicine Agency (EMA) emphasizes the limited experience in predictive modeling involving enzyme induction and inhibition ^[8]. Drug metabolism is often described by kinetic enzyme laws such as the Michaelis–Menten kinetics or the kinetic of competitive substrate inhibition ^[9]. The latter has been used to describe drug–drug-interactions (DDI) between dabrafenib and the anti-fungal drug ketoconazole ^[10].

2. Model Extensions Are Well Supported by Data

In this work, the mechanistic model of dabrafenib metabolism of Ouellet et al. ^[7] has been complemented with dabrafenib metabolites, CYP3A4 induction, and ketoconazole DDI. The Ouellet model is not detailing dabrafenib metabolites and describes enzyme induction only as a phenomenological equation ^[7] applying a dose and time-dependent clearance term. Enzyme regulation was not further elaborated therein. The Ouellet model allowed to recapitulate four different experiments with two-week periodic administration of dabrafenib in four different doses and for the initial dynamic phases and the steady-state conditions ^[7].

By not only considering the enzyme effect but also the presence of CYP3A4 in the free and bound form, the enzyme occupancy can be analyzed. Changes in occupancy of the enzyme directly impact CYPs enzyme capacity and explain DDI without requiring inhibitory enzyme kinetic terms. Consequently, the balance of already bound and still free CYP3A4 delivers an additional base for the mechanisms of enzyme induction. Indeed, the occupancy of enzymes is sufficient to describe the dabrafenib-ketoconazole DDI in the model. Relinquishing the simplifying assumptions for enzyme laws may be advised when enzyme occupancy is the expected core mechanism and simplifying assumptions cannot be maintained valid in subsequent model development. Eventually, the quantity and quality of data and underlying assumptions determine the degree to which mechanistic models based on reaction network theory may extrapolate, interpolate, and predict. This inductive research approach with mechanistic equation sets supports a more functional understanding than descriptive equations such as enzyme kinetic terms ^[11]^[12].

3. More Experimental Evidence Might Allow the Consideration of PXR

To model CYP3A4 induction, it was assumed that CYP3A4 levels remain constant as long as dabrafenib leaves sufficient CYP3A4 capacity to process other substances. Any drug using CYP3A4 reduces the enzyme capacity and, if insufficient, induces CYP3A4. This mechanism is functionally equivalent to the previously assumed direct dabrafenib-dependent CYP3A4 mRNA level increase via the pregnane X receptor (PXR) ^[13]. However, assuming a PXR mediated mechanism necessitates an additional model extension, which requires further data on PXR-dabrafenib binding and activation. Even then, an experimentally proven PXR activation in vitro does not necessarily confirm functional changes in vivo due to often multiple interactions of xenobiotic drugs ^[14]. Furthermore, in vitro data may only validate a lower or upper limit because additional molecules might further reduce enzyme capacity in vivo. Consequently, it seems reasonable to keep model assumptions sparse, to restrict quantitative data integration to clinical sources, and to use in vitro data only for structural model properties. Only with sufficient data, a PXR-dependent enzyme regulation can be considered in future studies.

4. Accumulating Carbo-Dabrafenib and Desmethyl-Dabrafenib Concentrations Are Plausible

Based on the currently recommended drug dose of 150 mg b.d dabrafenib, the model predicted an 8-fold accumulation of carbo-dabrafenib and desmethyl-dabrafenib compared to the respective initial Cmax. This implies that these metabolites were still present to relevant levels when the next dose was administered. Several clinical studies supported this model predictions. Furthermore, in response to a single dose of 95 mg radiolabeled dabrafenib solution, the total exposure (AUC) of carbo-dabrafenib was 4.9 times increased compared to dabrafenib ^[15].

The clinical relevance of this accumulation can be interpreted if the declining drug potency of the dabrafenib metabolites is considered: dabrafenib > hydroxy-dabrafenib ≈ desmethyl-dabrafenib ≫ carboxy-dabrafenib ^[15]. While carboxy-dabrafenib is not expected to contribute to the clinical activity because of the low potency of 1/22 compared to the parental drug ^[6], its abundance and potential conversion into clinical active desmethyl-dabrafenib are of particular interest.

5. Acidity Might Shift the Local Balance towards Active Desmethyl-Dabrafenib

Concomitant administration of ketoconazole and dabrafenib diminishes the levels of carbo-dabrafenib, while the model suggests the opposite. However, the model considers only the general acidity-dependent turnover from inactive carboxy-dabrafenib into active desmethyl-dabrafenib ^[15], while in reality, the rate constant in the pH-dependent Michaelis–Menten term may be time-variant or location-dependent. Tumour tissues, as well as cancer cells in vitro, are known to harbor a low pH environment ^[16]. The extracellular acidification rate of the melanoma cell lines FM55-M2 and SK-MEL-28 was shown to be 15-fold increased compared to primary melanocytes ^[17]. Thus, acidity-dependent metabolite transformation may lead to unknown local consequences at the tumor side and may also explain why desmethyl-dabrafenib accumulates more (12.6–35 fold increase) than carbo-dabrafenib (2.8–8.8 fold increase) ^[6].

6. Dabrafenib Is Ineffective If Highly Dosed in a Fibronectin-Supplemented Environment

It is demonstrated that low dabrafenib doses (10 nM) effectively reduce outgrowth of 451LU melanoma spheroids embedded into fibronectin-supplemented dextran hydrogels. In contrast, high dabrafenib concentrations (50 nM–100 nM) did not affect tumor outgrowth, while two melanoma spheroids even responded with accelerated outgrowth compared to untreated spheroids.

Recent studies in cancer research explained this effect to be dependent on the stiffness of the matrix and on the presence of fibronectin ^[18]. In this context, fibroblasts were shown to be able to switch the phenotype of melanoma cells to the mesenchymal state by shifting the signaling to the PI3K/mTOR pathway in a fibronectin-dependent manner ^[19]. The epithelial-mesenchymal transition as well as the metastatic potential were triggered by the mechanical characteristics of the microenvironment ^[20]^[21], causing a fibronectin-mediated transduction of the mechanical cues in the microenvironment into intracellular signals such as ERK, PI3K, ROCK-RHO through activation of the mechanosensor FAK. In parallel, mechanical cues also influenced Wnt and TGFB signaling thereby controlling YAP/TAZ mediated hippo signaling pathways ^[22]^[23]. It is increasingly accepted that tumor cells may develop non-cell autonomous resistance mechanisms ^[24], which not only requires targeting of the stroma ^[25] but also underlines the importance of three-dimensional cell culture-based screening systems for drug testing prior to their implementation into clinical trials ^[26].

7. Conclusions

The entry provides evidence that a dose of 150 mg b.d dabrafenib as currently administered to patients with BRAF-mutated malignant melanoma might be too high, and in the long run, may therefore lead to counter-intuitive ramifications. The dabrafenib is demonstrated to lose its anti-tumor activity when applied at higher doses to the metastatic melanoma cell line 451LU. It might be recommended to reduce the daily dose or to employ sequential therapy discontinuation to allow modulation of carbo-dabrafenib and desmethyl-dabrafenib levels in blood plasma. According to the drug selection study, 75 mg b.d turned out to be almost as beneficial as 150 mg b.d. dabrafenib in treating BRAF-mutated melanoma ^[6]. However, the low sample size, strong variability, and occasional dose escalation should be considered ^[6]. Hence, to further support the findings, experimental validation using additional BRAF-mutated melanoma cell lines or patient samples is recommended. Subsequently, a clinical study should be conducted to confirm safety and efficacy of a reduced dabrafenib dose, but also to provide further information on variability and population characteristics. While PBPK ^[27] and population models ^[7] may have more explanatory power to determine the individual dose decision, MCPK could support modeling of DDI and PK regarding drug metabolism with enzyme induction and hence provides a specialized niche in the comprehensive toolbox of contemporary pharmacokinetics.

References

Niessner, H.; Sinnberg, T.; Kosnopfel, C.; Smalley, K.S.; Beck, D.; Praetorius, C.; Mai, M.; Beissert, S.; Kulms, D.; Schaller, M.; et al. BRAF inhibitors amplify the proapoptotic activity of MEK inhibitors by inducing ER stress in NRAS-mutant melanoma. Clin. Cancer Res. 2017, 23, 6203–6214.
Niessner, H.; Schmitz, J.; Tabatabai, G.; Schmid, A.M.; Calaminus, C.; Sinnberg, T.; Weide, B.; Eigentler, T.K.; Garbe, C.; Schittek, B.; et al. PI3K pathway inhibition achieves potent antitumor activity in melanoma brain metastases in vitro and in vivo. Clin. Cancer Res. 2016, 22, 5818–5828.
Paluncic, J.; Kovacevic, Z.; Jansson, P.J.; Kalinowski, D.; Merlot, A.M.; Huang, M.L.H.; Lok, H.C.; Sahni, S.; Lane, D.J.; Richardson, D.R. Roads to melanoma: Key pathways and emerging players in melanoma progression and oncogenic signaling. Biochim. Biophys. Acta (BBA)-Mol. Cell Res. 2016, 1863, 770–784.
Larkin, J.; Minor, D.; D’Angelo, S.; Neyns, B.; Smylie, M.; Miller, W.H., Jr.; Gutzmer, R.; Linette, G.; Chmielowski, B.; Lao, C.D.; et al. Overall survival in patients with advanced melanoma who received nivolumab versus investigator’s choice chemotherapy in CheckMate 037: A randomized, controlled, open-label phase III trial. J. Clin. Oncol. 2018, 36, 383.
Long, G.V.; Stroyakovskiy, D.; Gogas, H.; Levchenko, E.; De Braud, F.; Larkin, J.; Garbe, C.; Jouary, T.; Hauschild, A.; Grob, J.J.; et al. Dabrafenib and trametinib versus dabrafenib and placebo for Val600 BRAF-mutant melanoma: A multicentre, double-blind, phase 3 randomised controlled trial. Lancet 2015, 386, 444–451.
Falchook, G.S.; Long, G.V.; Kurzrock, R.; Kim, K.B.; Arkenau, H.T.; Brown, M.P.; Hamid, O.; Infante, J.R.; Millward, M.; Pavlick, A.; et al. Dose selection, pharmacokinetics, and pharmacodynamics of BRAF-inhibitor Dabrafenib (GSK2118436). Clin. Cancer Res. 2014, 20, 4449–4458.
Ouellet, D.; Gibiansky, E.; Leonowens, C.; O’Hagan, A.; Haney, P.; Switzky, J.; Goodman, V.L. Population pharmacokinetics of dabrafenib, a BRAF inhibitor: Effect of dose, time, covariates, and relationship with its metabolites. J. Clin. Pharmacol. 2014, 54, 696–706.
Prueksaritanont, T.; Chu, X.; Gibson, C.; Cui, D.; Yee, K.L.; Ballard, J.; Cabalu, T.; Hochman, J. Drug–drug interaction studies: Regulatory guidance and an industry perspective. AAPS J. 2013, 15, 629–645.
Chou, T.-C.; Talaly, P. A simple generalized equation for the analysis of multiple inhibitions of Michaelis–Menten kinetic systems. J. Biol. Chem. 1977, 252, 6438–6442.
Zanger, U.M.; Schwab, M. Cytochrome P450 enzymes in drug metabolism: Regulation of gene expression, enzyme activities, and impact of genetic variation. Pharmacol. Ther. 2013, 138, 103–141.
van der Graaf, P.H.; Benson, N. Systems pharmacology: Bridging systems biology and pharmacokinetics-pharmacodynamics (PKPD) in drug discovery and development. Pharm. Res. 2011, 28, 1460–1464.
Wolkenhauer, O. Why model? Front. Physiol. 2014, 5, 21.
Suttle, A.B.; Grossmann, K.F.; Ouellet, D.; Richards-Peterson, L.E.; Aktan, G.; Gordon, M.S.; LoRusso, P.M.; Infante, J.R.; Sharma, S.; Kendra, K.; et al. Assessment of the drug interaction potential and single-and repeat-dose pharmacokinetics of the BRAF inhibitor dabrafenib. J. Clin. Pharmacol. 2015, 55, 392–400.
Wei, Y.; Tang, C.; Sant, V.; Li, S.; Poloyac, S.M.; Xie, W. A molecular aspect in the regulation of drug metabolism: Does PXR-induced enzyme expression always lead to functional changes in drug metabolism? Curr. Pharmacol. Rep. 2016, 2, 187–192.
Bershas, D.A.; Ouellet, D.; Mamaril-Fishman, D.B.; Nebot, N.; Carson, S.W.; Blackman, S.C.; Morrison, R.A.; Adams, J.L.; Jurusik, K.E.; Knecht, D.M.; et al. Metabolism and disposition of oral dabrafenib in cancer patients: Proposed participation of aryl nitrogen in carbon-carbon bond cleavage via decarboxylation following enzymatic oxidation. Drug Metab. Dispos. 2013, 41, 2215–2224.
Stubbs, M.; McSheehy, P.M.J.; Griffiths, J.R.; Bashford, C.L. Causes and consequences of tumour acidity and implications for treatment. Mol. Med. Today 2000, 6, 15–19.
Hall, A.; Meyle, K.D.; Lange, M.K.; Klima, M.; Sanderhoff, M.; Dahl, C.; Abildgaard, C.; Thorup, K.; Moghimi, S.M.; Jensen, P.B.; et al. Dysfunctional oxidative phosphorylation makes malignant melanoma cells addicted to glycolysis driven by the V600EBRAF oncogene. Oncotarget 2013, 4, 584.
Hirata, E.; Girotti, M.R.; Viros, A.; Hooper, S.; Spencer-Dene, B.; Matsuda, M.; Larkin, J.; Marais, R.; Sahai, E. Intravital imaging reveals how BRAF inhibition generates drug-tolerant microenvironments with high integrin β1/FAK signaling. Cancer Cell 2015, 27, 574–588.
Seip, K.; Fleten, K.G.; Barkovskaya, A.; Nygaard, V.; Haugen, M.H.; Engesæter, B.Ø; Mælandsmo, G.M.; Prasmickaite, L. Fibroblast-induced switching to the mesenchymal-like phenotype and PI3K/mTOR signaling protects melanoma cells from BRAF inhibitors. Oncotarget 2016, 7, 19997.
Wei, S.C.; Yang, J. Forcing through tumor metastasis: The interplay between tissue rigidity and epithelial–mesenchymal transition. Trends Cell Biol. 2016, 26, 111–120.
Weder, G.; Hendriks-Balk, M.C.; Smajda, R.; Rimoldi, D.; Liley, M.; Heinzelmann, H.; Meister, A.; Mariotti, A. Increased plasticity of the stiffness of melanoma cells correlates with their acquisition of metastatic properties. Nanomedicine 2014, 10, 141–148.
Northey, J.J.; Przybyla, L.; Weaver, V.M. Tissue force programs cell fate and tumor aggression. Cancer Discov. 2017, 7, 1224–1237.
Dupont, S.; Morsut, L.; Aragona, M.; Enzo, E.; Giulitti, S.; Cordenonsi, M.; Zanconato, F.; Le Digabel, J.; Forcato, M.; Bicciato, S.; et al. Role of YAP/TAZ in mechanotransduction. Nature 2011, 474, 179.
Levesque, M.P.; Cheng, P.F.; Raaijmakers, M.I.G.; Saltari, A.; Dummer, R. Metastatic melanoma moves on: Translational science in the era of personalized medicine. Cancer Metast. Rev. 2017, 36, 7–21.
Hutchenreuther, J.; Leask, A. Why target the tumor stroma in melanoma? J. Cell Commun. Signal 2018, 474, 113–118.
Astashkina, A.; Mann, B.; Grainger, D.W. A critical evaluation of in vitro cell culture models for high-throughput drug screening and toxicity. Pharmacol. Ther. 2012, 134, 82–106.
Yeo, K.R.; Jamei, M.; Yang, J.; Tucker, G.T.; Rostami-Hodjegan, A. Physiologically based mechanistic modelling to predict complex drug–drug interactions involving simultaneous competitive and time-dependent enzyme inhibition by parent compound and its metabolite in both liver and gut-the effect of diltiazem on the time-course of exposure to triazolam. Eur. J. Pharm. Sci. 2010, 39, 298–309.

© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.

Upload a video for this entry

Information

Subjects: Pharmacology & Pharmacy

Contributor MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :

Marco Albrecht

View Times: 383

Update Date: 29 Jan 2022

Table of Contents

Video Upload Options

Confirm